What are multi-agent systems?
Before defining multi-agent systems, it’s worth aligning on what an AI agent actually is.
An AI agent is a system that can take a goal, reason about the steps required, and carry those steps out by interacting with tools, data, or other systems.
In practice, this relies on three core capabilities:
Reasoning – breaking a problem into smaller, actionable steps
Action-taking – executing those steps rather than just describing them
Tool use – interacting with software, data sources, or workflows in the external environment
Today, most agents operate in narrow, well-defined contexts.
They coordinate parts of a workflow, generate structured outputs, update systems, or handle routine follow-ups.
Their value comes from reducing hand-offs and removing repetitive effort, often working quietly alongside individuals rather than replacing them.
You can already see this pattern inside organisations:
Microsoft Copilot turns meetings into summaries, tasks, and follow-ups, then drafts responses or documents using context from Outlook, Teams, and SharePoint
Cursor reviews code, proposes fixes, generates tests, and prepares structured pull requests as part of a continuous development workflow
Notion converts unstructured notes into tasks, project plans, or linked pages, keeping workspaces updated with minimal manual coordination
Each of these examples is a single agent focused on a specific set of tasks, operating within clear boundaries.
A multi-agent system is what happens when you move beyond one agent working alone, and instead coordinate multiple specialised agents, each responsible for a defined role, that collaborate toward a shared objective.
Rather than one agent trying to do everything, work is split across agents that:
focus on different stages of a process
operate on different data sources or tools
check, refine, or build on each other’s outputs
One agent might gather information, another might analyse it, a third might structure it into a report, and a fourth might trigger follow-up actions in downstream systems. Individually, each agent is simple and predictable.
Collectively, they behave like a coordinated workflow.
